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algorithm for solving the knapsack problem and developing application-speci c neural networks [34]. A two-layer sGA was used to represent the connectivity and weights of a feedforward neural network. Higher-level genes (connectivity) acted as a switch for sections of the lower-level weight representation. Sections of the weight level, whose corresponding connectivity bits were set to one, were expressed in the phenotype. Those whose corresponding bits had the value of zero were retained, but were not expressed. The main difference between the sGA applied to neuroevolution and the PBGA, is that the activation genes in the sGA act at the connection level, whereas the PBGAworks with neuron units, that is, functional units. This is a very relevant difference as, even though to enable/disable a neuron may seem much more disruptive, it permits preserving complex functional units. To demonstrate the potential of these types of structures when applied to neuroevolution, we have considered a GA that evolves the weights of feedforward arti cial neural networks (any other evolutionary algorithm with a similar type of encoding could have been chosen). These neural networks are encoded into sequences of genes for constructing a basic ANN unit. Each of these blocks is preceded by a gene promoter acting as an on/off switch that determines whether that particular unit will be expressed. For example, the genotypic representation used in the PBGA corresponding to the simple feedforward ANN shown in Figure 16.2 (top) is: 111W13 W23 1W14 W24 1W15 W25 1W36 W46 W56 1W37 W47 W57 1W38 W48 W58 1W69 W79 W89 asp.net pdf writer How to Easily Create a PDF Document in ASP . NET Core Web API
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